Empirical Modelling Methods Research Articles

Factors of psychological distress: clinical value, measurement substance, and methodological artefacts.

Psychometric models and statistical techniques are cornerstones of research into latent structures of specific psychopathology and general mental health. We discuss "pivot points" for future research efforts from a psychometric epidemiology perspective, emphasising sampling and selection processes of both indicators that guide data collection as well as samples that are confronted with them. First, we discuss how a theoretical model of psychopathology determines which empirical indicators (questions, diagnoses, etc.) and modelling methods are appropriate to test its implications. Second, we deal with how different research designs introduce different (co-)variances between indicators, potentially leading to a different understanding of latent structures. Third, we discuss widening the range of statistical models available within the "psychometrics class": the inclusion of categorical approaches can help to enlighten the debate on the structure of psychopathology and agreement on a minimal set of models might lead to greater convergence between studies. Fourth, we deal with aspects of methodology that introduce spurious (co-)variance in latent structure analysis (response styles, clustered data) and differential item functioning to gather more detailed information and to guard against over-generalisation of results, which renders assessments unfair. Building on established insights, future research efforts should be more explicit about their theoretical understanding of psychopathology and how the analysis of a given indicator-respondent set informs this theoretical model. A coherent treatment of theoretical assumptions, indicators, and samples holds the key to building a comprehensive account of the latent structures of different types of psychopathology and mental health in general.

Modelling an EDM Process Using Multilayer Perceptron Network, RSM, and High-Order Polynomial

Owing to the inadequacy of modelling electrical discharge machining (EDM) processes based on physical laws, three empirical modelling methods have been adopted in this study, namely, multilayer perceptron (MLP), response surface models (RSM), and high-order polynomials (HOP). To date, no publications regarding the use of the latter approach were found in the literature for modelling EDM processes; there were however some related to the approximation quality of RSM versus that of MLP networks but no investigations assessing the performance of the latter method against HOP. This study attempts to fill this gap by comparing the performance of the three methods mentioned above when modelling an EDM process with a WC-Co workpiece material. Three models were developed to correlate the material removal rate (MRR) with current, on-time, off-time, and capacitance. The half-normal plot and analysis of variance were used to test the significance of the investigated parameters. Due to the complex interdependence pattern that current and pulse on-time exhibited, the approximation of RSM was poor while that of the HOP and MLP models was adequate. A confirmation run based on new factor levels was performed to test the models’ generalization. The performance of the HOP model was marginally inferior to that of the MLP, but based on the paired �� -test, both models performed equally well.

A Instantaneous Availability Modelling Method for Repairable System

A Instantaneous availability modelling method for repair system was introduced. The empirical modeling method was used to the reliability function, and the life distribution selection method was proposed. Then, under the assumption that the maintainability was decreasing with the calendar time, the Fourier function was chosen for the basic fitting functions. Under the original definition of availability for the system, the availability modeling method was proposed. Finally, a case example for the method was given, and the result showed that the method was convenient for availability evaluation.

A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process

Due to the controversy associated with modelling Electrical Discharge Machining (EDM) processes based on physical laws; this task is predominantly accomplished using empirical modelling methods. The modelling studies reported in the literature deal predominantly with quantitative parameters i.e. ones with numerical levels. In fact, modelling categorical parameters has been devoted a scant attention. This study reports the results of an EDM experiment conducted on the Ti–6Al–4V alloy. Its aim was to model the relationship between the Material Removal Rate (MRR) and the parameters of the process, namely, current, pulse on-time and pulse off-time along with a categorical factor (electrode material). The modelling process was accomplished using adaptive neuro-fuzzy inference system (ANFIS) and polynomial modelling approaches. In fact, one purpose of this study was to compare the performance of these modelling approaches as no study was found contrasting their prediction capability in the literature. Regarding the polynomial model, two numerical parameters (current and pulse on-time) were declared significant in the ANOVA together with the electrode material and its interaction with pulse on-time. Thus, they were all incorporated in the developed polynomial model. Furthermore, five ANFIS models with 6, 9, 19, 21 and 51 rules were developed utilizing the first order Sugeno fuzzy approach by back-propagation neural networks training algorithm. Of these, the ANFIS model with 21rules was the best. This model also outperformed the polynomial model remarkably in terms of predicting error, residuals range and the correlation coefficient between the experimental and predicted MRR values. The study sheds light on the powerful learning capability of ANFIS models and its superiority over the conventional polynomial models in terms of modelling complex non-linear machining processes.

Empirical modelling methods to identify issues of concern in a two-sided military system

We propose the notion of a ‘substrate’ model which can be used as a common and agreed picture to develop issues of concern in the problem-structuring phase of a study. Such a process provides a transparent and defensible means to develop prioritised options for consideration by decision makers. In this paper, we explore two empirical methods, one based on sequential examination of events in a process and the other uses a functional (influence diagram like) approach to explore how system features change with time. An example is taken from a current problem in the military sphere—the two-sided interaction involving the use of, and defence against, improvised explosive devices. In this paper, we illustrate the strengths and weaknesses of the two modelling methods, the complementarity of the use of a more concrete ‘substrate model’ with the ‘softer’ aspects of problem structuring and the implications of the clients’ needs, in particular the position of the study within the typical Defence capability development life cycle. We believe that such empirical methods will have practical application to similar studies at the ‘front-end’.

Improvement of complex and refractory ecological models: Riverine water quality modelling using evolutionary computation

Complex environmental models have frequently suffered from large discrepancies between prediction and reality, inaccurate quantification of multivariate parameters, and difficulties in dealing with nonlinearities. We introduce an interdisciplinary project combining an ecological river-process model and evolutionary optimisation of model parameters, resulting in tools for more effective water resource management. The aim is to more tightly integrate the expert's knowledge and the evolutionary system through an iterated cycle of knowledge refinement and evolutionary search. This requires new methods to specify the expert knowledge in ways that can be integrated into the search. We used an evolutionary algorithm to optimise the multivariate values of the model parameters while retaining their acceptability, verifying that their ranges and values were consistent with ecological knowledge and constraints. The best model had a significantly lower predictive error than the initial process model parameterised from literature estimates. Its error was also over 50% less than those of the purely empirical modelling methods of linear regression and neural network learning. We conclude that combining process knowledge with evolutionary learning can play an important role in ecological modelling.

Model to Estimate Trip Distribution: Case Study of the Marmaray Project in Turkey

Istanbul, the only city that straddles two continents, Europe and Asia, is one of the biggest metropolitan areas in the world with over thirteen million residents. The demand for transportation in Istanbul has been boosted considerably due to the increasing population and rising vehicle ownership. On the other hand, the transportation infrastructure has not been developing sufficiently to meet this demand. Hence, the Turkish government and local authorities sought various alternatives to the road transportation system in Istanbul and came up with the Marmaray Project, which was planned and designed to serve both intraurban and interurban passengers and freight transport. One of the main aims of the Marmaray Project is to shift the traffic demand from roads to the rail network. In this paper, a travel demand modeling framework was developed to calculate the transportation demand of the Marmaray corridor. In addition, a model including empirical modeling methods was developed for the highways to estimate the origin and destination matrices. The developed model was used to estimate freight and passenger transportation between Istanbul and other Turkish provinces. The estimated transportation demand results were used to calculate the required train numbers on a daily basis through the Marmaray corridor and some suggestions were put forward to increase the capacity of this corridor.

AN EMPIRICAL MODELLING FRAMEWORK FOR FORECASTING FREIGHT TRANSPORTATION

This paper presents a framework which includes empirical modelling methods to estimate freight transportation between defined zones. In this method, observed origin and destination matrices for each type of freight are constituted based on the link counts and the roadside truck survey data. The gravity method is selected to estimate origin and destination matrices by using observed link flows, gross domestic product by provinces and interzonal distances. Advanced statistical techniques and regression analyses are used to estimate the coefficients of the gravity method. The final freight transportation matrix is calibrated with the link flows data by using iterative techniques. The developed method was applied to find the origin and destination matrix of the total freight transportation in Turkey and successful results were obtained.

Advantages and applications of transforming empirical model input space with dimensional models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a ‘Simple Committee’ technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the “Minimum Variance Committee” technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Estimation of freight transportation

Transportation is an important human activity which has many economic, social and other effects. In particular, freight transportation is economically very important for any country, and includes transporting a wide variety of goods ranging from raw materials to finished goods. These freights must be transported to the market efficiently and safely at reasonable cost. In addition, the external effects caused by freight transportation must be at an environmentally and socially acceptable level. To achieve these goals, an effective freight transportation planning model is required for forecasting the future trends of freight transportation. This paper presents a framework which includes empirical modelling methods to estimate the freight transportation between defined zones. The gravity method was used to estimate origin and destination matrices by using observed link flows, gross domestic product by provinces and interzonal distances. The coefficients of the gravity method were estimated by using advanced statistical techniques and regression analyses. The final freight transportation matrix was calibrated with the link flows data by using iterative techniques. The suggested framework was successfully applied to a data set provided by the Turkish General Directorate of Highways.

Urban heat island research of Novi Sad (Serbia): A review

In the second part of the 20th century, urbanization accelerated and reached enormous magnitude, which results more and more people live in urbanized regions. Nowadays, about half of the human population is affected by the burdens of urban environments and furthermore the modified parameters of the urban atmosphere compared to the natural environment. Novi Sad (45°15’N, 19°50’E) is located in the northern part of Serbia, i.e. on the southern part of the Pannonian Plain and it is the second largest city in the country with a population of about 320,000 in a built-up area of approximately 80 km2. The geographical area is plain, from 80 to 86 m a.s.l., with a gentle relief, so its climate is free from orographic effects. According to Koppen-Geiger climate classification, this region is categorised as Cfa climate (temperate warm climate with a rather uniform annual distribution of precipitation). In the last 20 years, a few papers have been published considering urban heat island (UHI) investigations of Novi Sad. The first publication in 1994 is theoretically based and presents all parameters, methods and measurements, which have to be used in order to work on UHI research of Novi Sad. The next studies from 1995 and 2006 analyzed various temperature parameters based on 30-40 year long time series and used rural and urban stations in order to get urban-rural temperature differences. Based on meteorological parameters and the structure of urban area, in 2010 the necessity of defining locations of an urban climate network was showed in order to advance further UHI research. In the last two publications from 2011 a new empirical modeling method, adjusted for cities located on plains, has been used in order to determine locations for representative stations of an urban climate network in Novi Sad.

Microvibration Model Development and Validation of a Cantilevered Reaction Wheel Assembly

Reaction wheel assemblies are one of the most important microvibration sources on typical modern satellites. In this paper microvibrations induced by a cantilevered reaction wheel assembly are modelled and validated against microvibration test results. The disturbance model is developed using energy method. A microvibration measurement platform is designed to measure its disturbances. Disturbance test results are analyzed in detail. The peculiar dynamic characteristics such as nonlinearity and high damping of harmonic responses in the test results are discussed. Estimations of damping values used in the disturbance model are introduced. A new method developed to model harmonic excitations is discussed. Furthermore, novel methods to identify harmonics and extract model parameters from test results are presented. The empirical modeling method developed for broadband noise excitations are also introduced and validated.

Prediction of Reliability Model for CNC Machine Tool Based on Exponential Smoothing Model

In order to manage equipment maintenance work and reduce enterprise cost, a new prediction method of reliability parameters is proposed based on failure time in this paper. The reliability model was built based on failure time, and the reliability parameters were obtained by the empirical modeling method. Then parameters from historical data were used as predictive model parameters, which applied the exponential smoothing methods to establish predictive models based on historical data. Finally, prediction model of reliability was built by the predicted parameters used the above method. With failure data of CNC machine tool as an example, analysis of living example indicated that the proposed method acted effectively, and the precision of prediction is quite ideal, which help consumers of CNC machine tool provide the decision support of equipment maintenance management; meanwhile, strengthen the enterprise's competitive power to some extent.

Challenges of Accurate Cost Estimation for Facilities

Cost estimation for facilities depends on early concept selection and critical inputs, often complicated by uncertainty in one or more of the critical inputs. Empirical cost models and cost modeling methods using these inputs vary in degrees of scope, comprehensiveness, and robustness. Cost modeling is critical in developing project economics to determine if a project is a sound investment. Its importance extends from concept comparison and selection to use in planning and optimization of field development, and to benchmarking of projected and actual costs. Although idiosyncrasies in projects and their required facilities drive cost estimations, differences in organizational capabilities further affect the validity and reliability of these estimations. This article will examine the industry’s approach to cost estimation, highlighting challenges and sources of error, and then discuss models specific to the Arctic and offshore Gulf of Mexico (GOM) regions.

Lloydminster, Saskatchewan Vertical Well SAGD Field Test Results

Summary Husky Energy's Pikes Peak steam project has been highly successful using CSS and follow-up steam drive for vertical and directional wells located on the bottomwater-free structural high inside the oil/water contact of the Waseca channel sand reservoir(1). In some areas, recoveries of up to 90% have been achieved with a cumulative steam/oil ratio (SOR) of less than 3.0. Use of CSS with follow-up drive has not been successful where bottomwater is present outside the oil/water contact. Some improvement in performance was achieved using large CSS slugs and high-steam injection rates, but in some locations CSS performances were still poor and conversion from CSS to drive has not been successful in any location. In 2008 Husky initiated a field test of a single vertical well SAGD configuration in an area outside the oil/water contact to evaluate an alternate process in which the "conventional" CSS performance of 10 wells was poor from the second or third stimulation onward. The results from the first single vertical SAGD well pilot demonstrated that a single vertical well SAGD configuration could be successfully completed and operated. The field data are being evaluated using empirical and numerical modelling methods to develop completion and operating strategies that optimize future produced oil rates and SORs. Another benefit of the field test was its demonstration of a low-cost and rapid method to help determine if a reservoir has sufficient vertical permeability for horizontal well SAGD to work.

Mathematical modelling of steam-water cycle with auxiliary empirical functions application

Mathematical modelling of steam-water cycle with auxiliary empirical functions applicationResearch oriented on identification of operating states variations with the application of mathematical models of thermal processes has been developed in the field of energy processes diagnostics. Simple models, characterised by short calculation time, are necessary for thermal diagnostics needs. Such models can be obtained using empirical modelling methods. Good results brings the construction of analytical model with auxiliary empirical built-in functions. The paper presents a mathematical model of a steam-water cycle containing mass and energy balances and semiempirical models of steam expansion line in turbine as well as heat transfer in exchangers. A model of steam expansion line in a turbine is worked out with the application of a steam flow capacity equation and an internal efficiency of process equation for each group of stages for the analysed turbine. A model of a heat exchanger contains energy balance and the relation describing heat transfer in an exchanger, proposed by Beckman. Estimation of empirical equations coefficients was realised with the application of special and reliable measurements. Estimation criterion was a weighted relative sum of the remainder squares. There are exemplary calculations results presented in the final part of paper.

Integrated estimation of measurement error with empirical process modeling—A hierarchical Bayes approach

AbstractAdvanced empirical process modeling methods such as those used for process monitoring and data reconciliation rely on information about the nature of noise in the measured variables. Because this likelihood information is often unavailable for many practical problems, approaches based on repeated measurements or process constraints have been developed for their estimation. Such approaches are limited by data availability and often lack theoretical rigor. In this article, a novel Bayesian approach is proposed to tackle this problem. Uncertainty about the error variances is incorporated in the Bayesian framework by setting noninformative priors for the noise variances. This general strategy is used to modify the Sampling‐based Bayesian Latent Variable Regression (Chen et al., J Chemom., 2007) approach, to make it more robust to inaccurate information about the likelihood functions. Different noninformative priors for the noise variables are discussed and unified in this work. The benefits of this new approach are illustrated via several case studies. © 2009 American Institute of Chemical Engineers AIChE J, 2009

Climate prediction for Brazil's Nordeste by empirical and numerical modeling methods

AbstractClimate prediction for Brazil's Nordeste meaningfully uses information through January to forecast the rainfall of March–June. Empirical methods developed at the University of Wisconsin use as predictors preseason (October–January) rainfall in the region and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific, as input to stepwise multiple regression to predict the March–June rainfall at a network of 27 stations. The training period is 1921–1957, the verification period 1958–1989, and real‐time forecasting continued to 2000. A numerical model (ECHAM4.5) experiment conducted at International Research Institute for Climate and Society at Columbia University used global SST information through January to predict the March–June rainfall at three gridpoints in the Nordeste for the years 1968–1999. For the same predictand, a complementary empirical experiment was conducted using the gridded rainfall observations, again with training period 1921–1957, to yield predictions for 1968–1999. Over this period predicted versus observed rainfall are evaluated in terms of correlation, root‐mean‐square error, absolute error, and bias. Compared with the empirical method the numerical modelling produces larger errors and negative bias. The empirical method captures 59% and the numerical modelling 49% of the variance. Copyright © 2008 Royal Meteorological Society

Ionospheric delay corrections for single-frequency GPS receivers over Europe using tomographic mapping

The majority of navigation satellites receivers operate on a single frequency and experience a positioning error due to the ionospheric delay. This can be compensated for using a variety of approaches that are compared in this paper. The study focuses on the last solar maximum. A 4D tomographic imaging technique is used to map the ionospheric electron density over the European region during 2002 and 2003. The electron density maps are then used to calculate the excess propagation delay on the L1 frequency experienced by GPS receivers at selected locations across Europe. The excess delay is applied to correct the pseudo-range single frequency observations at each location and the improvements to the resulting positioning are calculated. The real-time tomographic technique is shown to give navigation solutions that are better than empirical modelling methods and approach the accuracy of the full dual-frequency solution. The improvements in positioning accuracy vary from day to day depending on ionospheric conditions but can be up to 25 m during mid-day during these solar maximum conditions at European mid-latitudes.

Empirical modelling of tensile strength of woven fabrics

Aesthetic properties of fabrics have been considered as the most important fabric attribute for years. However, recently there has been a paradigm shift in the domain of textile material applications and consequently more emphasis is now being given on the mechanical and functional properties of fabrics rather than its aesthetic appeal. Moreover, in certain woven fabrics used for technical applications, strength is a decisive quality parameter. In this work, tensile strength of plain woven fabrics has been predicted by using two empirical modelling methods namely artificial neural network (ANN) and linear regression. Warp yarn strength, warp yarn elongation, ends per inch (EPI), picks per inch (PPI) and weft count (Ne) were used as input parameters. Both the models were able to predict the fabric strength with reasonably good precision although ANN model demonstrated higher prediction accuracy and generalization ability than the regression model. The warp yarn strength and EPI were found to be the two most significant factors influencing fabric strength in warp direction.